73 citations · 232 across the 28 of their papers we have counts for
10 papers · 1 filter
Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies
Francesco Denti, Stefano Peluso, Michele Guindani +1
The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly m…
Distributional Results for Model-Based Intrinsic Dimension Estimators
Francesco Denti, Diego Doimo, Alessandro Laio +1
Modern datasets are characterized by a large number of features that may conceal complex dependency structures. To deal with this type of data, dimensionality reduction techniques…
Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries
Louis Raynal, Sixing Chen, Antonietta Mira +1
Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC parameter estimation requires t…
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
Ritabrata Dutta, Karim Zouaoui-Boudjeltia, Christos Kotsalos +6
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffec…
A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data
Francesco Denti, Federico Camerlenghi, Michele Guindani +1
The use of high-dimensional data for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In p…
Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks
Francesco Bartolucci, Antonietta Mira, Stefano Peluso
A new modeling framework for bipartite social networks arising from a sequence of partially time-ordered relational events is proposed. We directly model the joint distribution of…
Likelihood-free parameter estimation for dynamic queueing networks: case study of passenger flow in an international airport terminal
Anthony Ebert, Ritabrata Dutta, Kerrie Mengersen +3
Dynamic queueing networks (DQN) model queueing systems where demand varies strongly with time, such as airport terminals. With rapidly rising global air passenger traffic placing i…
Flexible model selection for mechanistic network models
Sixing Chen, Antonietta Mira, Jukka-Pekka Onnela
Network models are applied across many domains where data can be represented as a network. Two prominent paradigms for modeling networks are statistical models (probabilistic model…
Fast Maximum Likelihood estimation via Equilibrium Expectation for Large Network Data
Maksym Byshkin, Alex Stivala, Antonietta Mira +2
A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural propertie…
Adaptive Incremental Mixture Markov chain Monte Carlo
Florian Maire, Nial Friel, Antonietta Mira +1
We propose Adaptive Incremental Mixture Markov chain Monte Carlo (AIMM), a novel approach to sample from challenging probability distributions defined on a general state-space. Whi…